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meta-learning differee (train_utils.py)
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1 changed files with 4 additions and 3 deletions
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@ -327,7 +327,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
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#print(len(model['model']['functional']._fast_params),"step", time.process_time()-t)
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#print(len(model['model']['functional']._fast_params),"step", time.process_time()-t)
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if(high_grad_track and i>0 and i%inner_it==0): #Perform Meta step
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if(high_grad_track and i>0 and i%inner_it==0 and epoch>=opt_param['Meta']['epoch_start']): #Perform Meta step
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#print("meta")
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#print("meta")
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val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val) + model['data_aug'].reg_loss()
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val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val) + model['data_aug'].reg_loss()
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#print_graph(val_loss) #to visualize computational graph
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#print_graph(val_loss) #to visualize computational graph
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@ -349,9 +349,10 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
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model['model'].detach_()
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model['model'].detach_()
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meta_opt.zero_grad()
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meta_opt.zero_grad()
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elif not high_grad_track:
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elif not high_grad_track or epoch<opt_param['Meta']['epoch_start']:
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#diffopt.detach_()
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diffopt.detach_()
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model['model'].detach_()
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model['model'].detach_()
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meta_opt.zero_grad()
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tf = time.perf_counter()
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tf = time.perf_counter()
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